/* 
 * This file is part of the UEA Time Series Machine Learning (TSML) toolbox.
 *
 * The UEA TSML toolbox is free software: you can redistribute it and/or 
 * modify it under the terms of the GNU General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or 
 * (at your option) any later version.
 *
 * The UEA TSML toolbox is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License along
 * with the UEA TSML toolbox. If not, see <https://www.gnu.org/licenses/>.
 */
 
package tsml.filters.shapelet_filters.old_code;

import java.io.FileWriter;
import java.io.PrintWriter;
import java.util.ArrayList;
import java.util.Arrays;

import tsml.transformers.shapelet_tools.Shapelet;
import tsml.filters.shapelet_filters.ShapeletFilter;
import tsml.transformers.shapelet_tools.distance_functions.OnlineShapeletDistance;
import weka.core.Attribute;
import weka.core.Instances;
import weka.filters.SimpleBatchFilter;
import tsml.transformers.shapelet_tools.quality_measures.ShapeletQuality.ShapeletQualityChoice;

/**
 *      * copyright: Anthony Bagnall
 * A filter for using the shapelet transform with hierarchical
 * clustering of shapelets.
 * 
 * Recommended usage: Build the shapelet transform outside of this class and pass in. 
 * 
 * FullShapeletTransform shape=new  FullShapeletTransform();
 * //Build and use shape here
 *
 * int nosClusters=10;
 * ClusteredShapeletTransform cShape=new ClusteredShapeletTransform(shape,nosClusters);
 * 
 * it will work like this with any of the numerous constructors
  * ClusteredShapeletTransform cShape=new ClusteredShapeletTransform();
  * Instances c=cShape.process(data)
  * 
  * 
 
 * @author Jon Hills - j.hills@uea.ac.uk
 */
public class ClusteredShapeletTransform extends SimpleBatchFilter{
    
    ShapeletFilter st;
    protected double[][] distanceMap;
    protected ArrayList<int[]> clusterPairs;
    protected ArrayList<Shapelet> clusteredShapelets;
    protected ArrayList<Shapelet> allShapelets;    
    protected int noClust;
    public static int DEFAULT_NUMCLUSTERS=1;
/* 
 * 
 */
    public ClusteredShapeletTransform(ShapeletFilter shapes, int n){
        st=shapes;
        this.clusteredShapelets = new ArrayList<Shapelet>();
        noClust=n;
    }
    /**
     * Fully specified constructor.
     * 
     * @param k The number of shapelets to store.
     * @param minShapeletLength The minimum shapelet langth.
     * @param maxShapeletLength The maximum shapelet length.
     * @param qualityChoice The quality measure to use for assessing candidates.
     * @param noClust The number of clusters.
     */
    public ClusteredShapeletTransform(int k, int minShapeletLength, 
            int maxShapeletLength, ShapeletQualityChoice qualityChoice,
            int noClust) {
        st=new ShapeletFilter(k, minShapeletLength, maxShapeletLength, qualityChoice);
        this.noClust=noClust;
        this.clusteredShapelets = new ArrayList<Shapelet>();
    }
    
    /**
     * Partially specified constructor. Defaults to clustering. If
     * clustering is used, defaults to one cluster, i.e., the best
     * shapelet only.
     * 
     * @param k The number of shapelets to store.
     * @param minShapeletLength The minimum shapelet langth.
     * @param maxShapeletLength The maximum shapelet length.
     * @param qualityChoice The quality measure to use for assessing candidates.
     */
    public ClusteredShapeletTransform(int k, int minShapeletLength, 
            int maxShapeletLength, ShapeletQualityChoice qualityChoice) {
        st=new ShapeletFilter(k, minShapeletLength, maxShapeletLength, qualityChoice);
        st.setSubSeqDistance(new OnlineShapeletDistance());
        this.noClust=DEFAULT_NUMCLUSTERS;
        this.clusteredShapelets = new ArrayList<Shapelet>();
    }

    /**
     * Partially specified constructor. Defaults to Information Gain quality
     * measure. Defaults to no clustering. If clustering is used, defaults to 
     * one cluster, i.e., the best shapelet only.
     * 
     * @param k The number of shapelets to store.
     * @param minShapeletLength The minimum shapelet langth.
     * @param maxShapeletLength The maximum shapelet length.
     */
    public ClusteredShapeletTransform(int k, int minShapeletLength, 
            int maxShapeletLength) {
        st=new ShapeletFilter(k, minShapeletLength, maxShapeletLength);
        st.setSubSeqDistance(new OnlineShapeletDistance());
        this.noClust=DEFAULT_NUMCLUSTERS;
        this.clusteredShapelets = new ArrayList<Shapelet>();
    }
    
    /**
     * Partially specified constructor. Defaults to Information Gain quality
     * measure. 
     * 
     * @param k The number of shapelets to store.
     * @param minShapeletLength The minimum shapelet langth.
     * @param maxShapeletLength The maximum shapelet length.
     * @param noClust The number of clusters.
     */
    public ClusteredShapeletTransform(int k, int minShapeletLength, 
            int maxShapeletLength, int noClust) {
        st=new ShapeletFilter(k, minShapeletLength, maxShapeletLength);
        st.setSubSeqDistance(new OnlineShapeletDistance());
        this.noClust=noClust;
        this.clusteredShapelets = new ArrayList<Shapelet>();
    }

    /**
     * Partially specified constructor. Defaults to Information Gain quality
     * measure. Minimum and maximum shapelet lengths must be set before use.
     * Defaults to no clustering. Defaults to one cluster.
     * 
     * @param k The number of shapelets to store.
     */
    public ClusteredShapeletTransform(int k) {
        st=new ShapeletFilter(k);
        st.setSubSeqDistance(new OnlineShapeletDistance());
        this.noClust=DEFAULT_NUMCLUSTERS;
        this.clusteredShapelets = new ArrayList<Shapelet>();
    }
    
    /**
     * Partially specified constructor. Defaults to Information Gain quality
     * measure. Minimum and maximum shapelet lengths must be set before use.
     * 
     * @param k The number of shapelets to store.
     * @param cluster Whether or not to use clustering. 
     * @param noClust Then number of clusters.
     */
    public ClusteredShapeletTransform(int k, boolean cluster, int noClust) {
        st=new ShapeletFilter(k);
        st.setSubSeqDistance(new OnlineShapeletDistance());
        this.noClust=noClust;
        this.clusteredShapelets = new ArrayList<Shapelet>();
    }
    
    /**
     * Empty constructor. Defaults to Information Gain quality measure, no
     * clustering, one cluster if clustering turned on. Shapelet lengths must
     * be set. K must be set.
     */
    public ClusteredShapeletTransform()
    {
        st=new ShapeletFilter();
        st.setSubSeqDistance(new OnlineShapeletDistance());
        this.noClust=DEFAULT_NUMCLUSTERS;
        this.clusteredShapelets = new ArrayList<Shapelet>();
    }
    
    /**
     * Transform datasets. If cluster=true, shapelets will be clustered into
     * noClust clusters prior to transformation.
     * 
     * @param data - the input data to be transformed (and to find the shapelets if this is the first run)
     * @return the transformed Instances in terms of the distance from each shapelet
     * @throws Exception - if the number of shapelets or the length parameters specified are incorrect
     */
    @Override
    public Instances process(Instances data) throws Exception{
        int size=st.getNumberOfShapelets();
        if(size < 1)
            throw new Exception("Number of shapelets initialised incorrectly - please select value of k (Usage: setNumberOfShapelets");
        if(size<noClust)
           throw new Exception("Trying to produce more clusters than there are shapelets!");
 // We only want the shapelets from st, so could optimize this to not work out the transform too. However, cleaner this way          
        if(!st.isFirstBatchDone())
            st.process(data);
        allShapelets=st.getShapelets();
        clusterShapelets();
        
        this.st.setShapelets(clusteredShapelets);
        return st.buildTansformedDataset(data);
    }
    
    /**
     *
     * @param inputFormat - the format of the input data
     * @return a new Instances object in the desired output format
     * @throws Exception - if all required attributes of the filter are not initialised correctly
     */
    @Override
    protected Instances determineOutputFormat(Instances inputFormat) throws Exception{

        int s=st.getNumberOfShapelets();
        if(s < 1 || s<noClust){
            throw new Exception("ShapeletFilter not initialised correctly - please specify a value of k that is greater than or equal to 1. You entered s="+s+" num clusters ="+noClust);
        }

        ArrayList<Attribute> atts = new ArrayList<>();
        String name;
        for(int i = 0; i < noClust; i++){
            name = "CShapelet_" + i;
            atts.add(new Attribute(name));
        }
        Attribute target = inputFormat.attribute(inputFormat.classIndex());

        ArrayList<String> vals = new ArrayList(target.numValues());
        for(int i = 0; i < target.numValues(); i++){
            vals.add(target.value(i));
        }
        atts.add(new Attribute(inputFormat.attribute(inputFormat.classIndex()).name(), vals));
        Instances result = new Instances("CShapelets" + inputFormat.relationName(), atts, inputFormat.numInstances());
        result.setClassIndex(result.numAttributes() - 1);
        return result;
    }
    
    /**
     *  Creates a set of clustered shapelets with a noClust clusters.
     */
    public void clusterShapelets()
    {
//       System.out.println("Clustering shapelets: "+this.noClust);
       
       double[][] shapeletSet = new double[allShapelets.size()][];
       
       for(int i=0;i<shapeletSet.length;i++)
       {
           shapeletSet[i] = allShapelets.get(i).getUnivariateShapeletContent();
       }
       
       distanceMap = getDistanceMap(shapeletSet);
       clusterPairs = new ArrayList();
       this.clusteredShapelets.clear();
       
       //Adds an int[] of each index to clusterPairs
       for(int i=0;i<distanceMap.length;i++)
       {
           int[] tmp = {i};
           clusterPairs.add(tmp);
       }
       
       //Returns pair of indexes to clusterPairs/adjusted distanceMap
       //Is the index of the ArrayList ever a factor? It should be done with
       //just the stored indexes.
       int[] bestPair = findClosestPair(distanceMap);
       double[][] map = new double[2][];
       
       while(clusterPairs.size()>noClust)
       {
           adjustClusterPairs(bestPair);
           map = adjustDistanceMap();
           bestPair = findClosestPair(map);
       }              
       
       //Select the best shapelet in each cluster
       //Make sure that the index stored in clusterPairs is the index of
       //the shapelet stored in the shapelet ArrayList.
       for(int i=0;i<clusterPairs.size();i++)
       {
           if(clusterPairs.get(i).length==1)
               clusteredShapelets.add(allShapelets.get(clusterPairs.get(i)[0]));
           else
           {
               double best = Double.MIN_VALUE;
               int position = 0;
               
               for(int j=0;j<clusterPairs.get(i).length;j++)
               {
                   //Infogain will need to be changed to quality measure
                   if(allShapelets.get(clusterPairs.get(i)[j]).qualityValue >best)
                   {
                       best = allShapelets.get(clusterPairs.get(i)[j]).qualityValue;
                       position =j;
                   }
               }
              
               clusteredShapelets.add(allShapelets.get(clusterPairs.get(i)[position]));
               //System.out.println("Added shapelet at position"+position);
           }
           
       }
             
    }
    
    /**
     * Finds the pair on a distance map with the least distance between them.
     * 
     * @param map The current distance map
     * @return The indexes of the best-matching pair.
     */
    private int[] findClosestPair(double[][] map)
    {
        int[] pair = new int[2];
        double best = Double.MAX_VALUE;
        
        for(int i=0;i<map.length;i++)
        {
            for(int j=i+1;j<map[i].length;j++)
            {
                if(map[i][j]<best)
                {
                    best = map[i][j];
                    pair[0] = i;
                    pair[1] = j;
                }
            }
        }
        
        
        return pair;
    }
    
    
    /**
     * Creates complete distance map with identities and redundant information.
     * 
     * @param shapeletSet An array of shapelet content double arrays.
     * @return The distance map for the shapelet set.
     */
    private double[][] getDistanceMap(double[][] shapeletSet)
    {
        
        double[][] map = new double[shapeletSet.length][];
        
        //Initialise double[]
        for(int i=0;i<shapeletSet.length;i++)
        {
            double[] tmp = new double[shapeletSet.length];
            map[i] = tmp;
        }
        
        for(int i=0;i<shapeletSet.length;i++)
        {
            map[i][i] = 0;
            
            
            for(int j=i+1;j<shapeletSet.length;j++)
            {
                map[i][j] = findMinDistance(shapeletSet[i],shapeletSet[j]);
                map[j][i] = map[i][j];
            }
            
        }
        
        
        return map;
    }
    
    /**
     * Returns the shapelet distance between two shapelets, that is, the
     * shortest distance between the shorter shapelet and the best-matching
     * subsequence of the longer shapelet.
     * 
     * @param first One shapelet content array.
     * @param second The other shapelet content array.
     * @return The shapelet distance between the shapelets.
     */
    private double findMinDistance(double[] first, double[] second)
    {
        
        double distance = 0;
        double bestDist = Double.MAX_VALUE;
        
        if (first.length == second.length){
            bestDist = getDistance(first,second);
        }
        else{
            if(first.length>second.length){
                for(int i=0;i<(first.length-second.length)+1;i++){
                    double [] temp= Arrays.copyOfRange(first, i, i+second.length);
                    distance = getDistance(temp,second);
                    if(distance<bestDist)
                        bestDist=distance;
                }
            }
            else{
                for(int i=0;i<(second.length-first.length)+1;i++){

                    double [] temp= Arrays.copyOfRange(second, i, i+first.length);
                    distance = getDistance(temp,first);
                    
                    if(distance<bestDist)
                        bestDist=distance;
                }
            }
        }
        
        return bestDist;
    }
    
    /**
     * Returns squared Euclidean distance between two series of equal length.
     * 
     * @param first The first series.
     * @param second The second series.
     * @return The Euclidean distance between the series.
     */
    private double getDistance(double[] first, double[] second){
        double distance = 0;        
        for(int i=0;i<first.length;i++)
            distance = distance+ ((first[i]-second[i])*(first[i]-second[i]));
        
        return Math.sqrt(distance);
    }
    
    /**
     * Rebuilds distance map from scratch - not efficient.
     * 
     * @return The adjusted distance map.
     */
    private double[][] adjustDistanceMap()
    {
        double[][] map = new double[clusterPairs.size()][];
        
        //Initialise distance map
        for(int i=0;i<map.length;i++)
        {
            double[] tmp=new double[clusterPairs.size()];
            map[i] = tmp;
        }
        
        //Retrieve distances from original distance map.
        for(int i=0;i<clusterPairs.size();i++)
        {
            
            map[i][i]=0;
            
            for(int j=i+1;j<clusterPairs.size();j++)
            {
                map[i][j] = averageDistance(clusterPairs.get(i),clusterPairs.get(j));
                map[j][i] = map[i][j];
            }
        }
        
        return map;
    }
    
    /**
     * Returns the average distance for the distance map.
     * 
     * @param first First cluster.
     * @param second Second cluster.
     * @return Average distance.
     */
    private double averageDistance(int[] first,int[] second)
    {
        double dist = 0;        
            
        for(int i=0;i<first.length;i++)
        {
            for(int j=0;j<second.length;j++)
            {
                dist = dist+distanceMap[first[i]][second[j]];
            }
        }
        
        dist = dist/(first.length*second.length);
        
        return dist;
    }
    
    //
    
    /**
     * Takes a pair of indexes to the clusterPair ArrayList and 
     * merges the entries.
     * 
     * @param pair A pair of indexes to the clusterPair ArrayList. 
     */
    private void adjustClusterPairs(int[] pair)
    {
       int[] first = clusterPairs.get(pair[0]);
       int[] second = clusterPairs.get(pair[1]);
       
       int[] tmp = new int[first.length+second.length];
       
       for(int i=0;i<tmp.length;i++)
       {
           if(i<first.length)
           {
               tmp[i]=first[i];
           }
           else
           {
               tmp[i]=second[i-first.length];
           }
       }
       
       clusterPairs.remove(pair[0]);
       clusterPairs.add(pair[0],tmp);
       clusterPairs.remove(pair[1]);
    
    }

    
    /**
     * Returns the noClust variable.
     * 
     * @return noClust.
     */ 
    public int getNoClust() 
    {
        return this.noClust;
    }
    /**
     * Sets the number of clusters to use.
     * 
     * @param num The number of clusters.
     */
    public void setNoClust(int num){
        this.noClust = num;
    }
    public void setShapeletTransform(ShapeletFilter s){
        st=s;
    }
    
    public void outputLog(String outfile) throws Exception
    {
        PrintWriter cout = new PrintWriter( new FileWriter(outfile), true);
        
        for(int i=0;i<clusteredShapelets.size();i++)
        {
//            System.out.println("******************************************");
            Shapelet s = clusteredShapelets.get(i);
            cout.println(s.qualityValue+","+s.seriesId+","+s.startPos);
            cout.flush();
            double[] con = s.getUnivariateShapeletContent();
            
            cout.print(con[0]);
            cout.flush();
            
            for(int j=1;j<con.length;j++)
            {
                cout.print(","+con[j]);
                cout.flush();
            }
 
            cout.println();
            cout.flush();
        }
        
        cout.close();
    }

    @Override
    public String globalInfo() {
        throw new UnsupportedOperationException("Not supported yet.");
    }
    
}
